CRITICAL (Test) | Collaborative Resource for Intensive-care Translational science, Informatics, Comprehensive Analytics, and Learning

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DATA CODEBOOK

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FAQ

Collaborative Resource for Intensive-care Translational science, Informatics, Comprehensive Analytics, and Learning

Translational research in artificial intelligence (AI) has been hindered by the lack of shared data resources with sufficient depth, breadth, and diversity. Our vision is to leverage nationwide CTSA sites (Northwestern, Tufts, WUSTL, UAB) with diverse racial, ethnic, and geographic profiles in order to develop and evaluate a Collaborative Resource for Intensive-care Translational science, Informatics, Comprehensive Analytics, and Learning, hence titled CRITICAL.

Get Access to the Data

For Individuals

Faculty, students, and staff at U.S. accredited universities can apply for access to the CRITICAL data.

First, check if your institution already has a data use agreement (DUA) in place with the CRITICAL Consortium: Institution Lookup

If your institution has a DUA in place, [submit an application]
If your institution does not have a DUA in place yet, [work with your institutional representatives to submit the agreement]

For Institutions

U.S. accredited universities who are capable of complying with the [CRITICAL Consortium’s Data Use Agreement](link to document) are eligible to apply for access for full-time faculty, students, and staff.

[Apply for access for your institution’s faculty, students, and staff]

About CRITICAL

Sponsored by the NIH National Center for Advancing Translational Sciences, CRITICAL is the first cross-CTSA attempt to create a multi-site, multi-modal, de-identified dataset that has both deep-data depth and broad-data width, the combination of which is still a major unmet need.

The dataset includes large quantities of longitudinal in-patient and out-patient data, both pre- and post- ICU admissions, from over 400,000 distinct critical-care patients, which makes it the largest publicly shared, disease-independent benchmarking clinical dataset yet created. The diversified racial, ethnic, and geographic profiles of the data are expected to answer urgent and long-standing clinical problems and to support fair and generalizable AI translation for advanced patient monitoring and decision support.

CRITICAL lends itself not only to AI and machine-learning (ML) research but also to outcomes-related research, opening the clinical translation to broader research communities.